In recent years, optical computing techniques have been developed for applications in the Oil and Gas Industry in the form of optical sensors on downhole or surface equipment to evaluate a variety of fluid properties. An optical computing device, also referred to herein as photometric system, is a device configured to receive an input of electromagnetic radiation from a substance or sample of the substance and produce an output of electromagnetic radiation from a processing element, also referred to as an optical element. The optical element may be, for example, a narrow band optical filter or an Integrated Computational Element (“ICE”) (also known as a Multivariate Optical Element (“MOE”). The design and operation of ICEs are described in, for example, U.S. Pat. Nos. 6,198,531; 6,529,276; and 8,049,881, each being owned by the Assignee of the present invention, Halliburton Energy Services, Inc., of Houston, Tex., the disclosure of each being hereby incorporated by reference in its entirety.
Fundamentally, optical computing devices utilize optical elements to perform calculations, as opposed to the hardwired circuits of conventional electronic processors. When light from a light source interacts with a substance, unique physical and chemical information about the substance is encoded in the electromagnetic radiation that is reflected from, transmitted through, or radiated from the sample. This information is often referred to as the substance's spectral “fingerprint.” Thus, the optical computing devices, through use of the optical elements and multiple detectors, are capable of extracting the information of the spectral fingerprint of multiple characteristics or analytes within a substance and converting that information into detectable output signals reflecting the overall properties of a sample.
One objective of the photometric system design is to optimize optical element selection by adequately characterizing a specified number of analytes. The other objective is to determine the number of actual optical elements needed for system implementation. The first objective is typically a global optimization problem and is often a starting point of system design to evaluate the entire candidate domain for future implementation. The second objective is dependent on the constraints of the tool or instrument and, therefore, requires conditional optimization with a fixed number of elements to cost effectively meet the implementation specification. The photometric system design also includes optimization of other system components and optical path for signal to noise ratio (“SNR”) and reliability enhancement.
Conventional photometric system design approaches are disadvantageous for a variety of reasons. In general, prior art approaches are closely related to the practice of variable selection in chemometrics. However, the existing software is designed for general purpose use, and does not automate global optimization and conditional optimization for specific application in photometric system design. The conventional software also has very limited functionality to relate selection optimization with environmental factors, such as, for example, the downhole environment in which the photometric system will be deployed. All components of the photometric system will experience downhole temperature variations from approximately 65° C. to 175° C. or higher, in addition to violent vibration during use. In addition, the spectroscopic cell will experience a pressure differential of up to 30,000 psi or higher. Therefore, the design optimization software should be capable of evaluating ruggedized designs to ensure sensor functionality and system reliability.
In view of the foregoing, there is a need in the art for a photometric system design optimization technique which environmentally ruggedizes the system while also possessing novel functionalities to provide integrated solutions.